Dirty Data, Broken Trust

Restore faith in your decisions with data management

At the heart of every decision your organization makes is data. No matter how well your business processes are designed, or how advanced your analytics strategy is, the integrity of the underlying data determines whether you thrive or falter. When your organization reaches out to prospects using invalid contact details, marketing dollars are wasted and sales revenues are lost. When executives make decisions using erroneous data, bad plans and strategies drain productivity and waste resources. When help desk and support staff have incorrect customer information, poor service experience decreases loyalty and retention. University of Chicago historian Daniel J. Boorstin once said, “The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.”

When enterprise data is incorrect or incomplete, it leads to one bad decision after another. This diminishes confidence among your information consumers and has a profound negative impact on your bottom line. Who’s affected by bad data? Everyone. From the marketing manager using outdated prospect details to promote a new product, to the physician caring for a patient with a chronic condition without a full history of treatments received in other facilities, or the shop floor manager struggling to identify the underlying causes of product defects with incomplete warranty claim data. Scenarios like these cause stakeholders to lose trust in the data they rely on to make important decisions. This white paper from Information Builders will discuss how this mistrust destroys economic value. We’ll outline the tangible and measurable costs of poor data quality, and highlight strategies and solutions for restoring trust and eliminating risk through improved data management.

Bad Data: The Impact Is Bigger Than You Think

Bad data currently exists in almost every organization. Whether it’s missing, incorrect, incomplete, or inconsistent across sources, as much as 32 percent of your data is likely in pretty poor shape.1 If left unchecked, the amount of corrupt or invalid information you have – and the damage it causes – will continue to grow as your data volumes increase. The causes of bad data are vast and varied. Among the most common are human error (which accounts for 59 percent of all data integrity problems) and multiple databases (which 42 percent of companies blame for their data quality headaches).2 Similar information, such as customer contact details, is often redundant or inconsistent across these diverse and disjointed databases.

But regardless of the underlying reasons, the results are troubling from both a business and a financial perspective:

■ 77 percent of companies believe their bottom line is affected by inaccurate and incomplete contact data

■ Ovum Research claims that companies lose about 30 percent of their revenues due to poor data quality

■ Regulatory fines, monetary losses from bad business decisions, and legal fees resulting from data errors can add up to millions of dollars, with IBM estimating the total cost to U.S. organizations to be $3.1 trillion dollars annually

■ Critical errors make up 10 to 25 percent of the average marketing database

These statistics are just the tip of the iceberg. All departments and functions will feel the impact when bad data flows into and across your enterprise.